Relational tree ensembles and feature rankings

[1]  Yao Wang,et al.  Attributed Heterogeneous Network Embedding Based on Graph Convolutional Neural Network , 2021, 2021 International Conference on Communications, Information System and Computer Engineering (CISCE).

[2]  Nada Lavrac,et al.  Propositionalization and embeddings: two sides of the same coin , 2020, Machine Learning.

[3]  Sebastijan Dumancic,et al.  From Statistical Relational to Neuro-Symbolic Artificial Intelligence , 2020, IJCAI.

[4]  Yu He,et al.  HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding , 2019, CIKM.

[5]  LazyBum: Decision tree learning using lazy propositionalization , 2019, ILP.

[6]  Dragi Kocev,et al.  Feature ranking for multi-target regression , 2019, Machine Learning.

[7]  Georg Langs,et al.  Causability and explainability of artificial intelligence in medicine , 2019, WIREs Data Mining Knowl. Discov..

[8]  Michelangelo Ceci,et al.  Ensemble Learning for Multi-Type Classification in Heterogeneous Networks , 2018, IEEE Transactions on Knowledge and Data Engineering.

[9]  Philippe Cudré-Mauroux,et al.  Are Meta-Paths Necessary?: Revisiting Heterogeneous Graph Embeddings , 2018, CIKM.

[10]  Jiawei Han,et al.  Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery , 2018, ECML/PKDD.

[11]  Nada Lavrac,et al.  Targeted End-to-End Knowledge Graph Decomposition , 2018, ILP.

[12]  Vincenzo Lagani,et al.  Feature selection for high-dimensional temporal data , 2018, BMC Bioinformatics.

[13]  Max Welling,et al.  Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.

[14]  Michelangelo Ceci,et al.  Multi-type clustering and classification from heterogeneous networks , 2018, Inf. Sci..

[15]  Nitesh V. Chawla,et al.  metapath2vec: Scalable Representation Learning for Heterogeneous Networks , 2017, KDD.

[16]  Saěso Dězeroski.  Relational Data Mining , 2001, Encyclopedia of Machine Learning and Data Mining.

[17]  Amedeo Napoli,et al.  Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data , 2016, Front. Mol. Biosci..

[18]  Peter Szolovits,et al.  Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer , 2016, Artif. Intell. Medicine.

[19]  Michelangelo Ceci,et al.  Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction , 2013, BMC Bioinformatics.

[20]  Philip S. Yu,et al.  Meta path-based collective classification in heterogeneous information networks , 2012, CIKM.

[21]  Michelangelo Ceci,et al.  Network regression with predictive clustering trees , 2011, Data Mining and Knowledge Discovery.

[22]  Nitesh V. Chawla,et al.  Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science , 2011, Stat. Anal. Data Min..

[23]  Jiawei Han,et al.  Ranking-based classification of heterogeneous information networks , 2011, KDD.

[24]  P. Geurts,et al.  Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.

[25]  Yizhou Sun,et al.  Graph Regularized Transductive Classification on Heterogeneous Information Networks , 2010, ECML/PKDD.

[26]  Andreas Bender,et al.  Predicting the functions of proteins in Protein-Protein Interaction networks from global information , 2009, MLSB.

[27]  Michelangelo Ceci,et al.  An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting , 2009, Discovery Science.

[28]  Michelangelo Ceci,et al.  A relational approach to probabilistic classification in a transductive setting , 2009, Eng. Appl. Artif. Intell..

[29]  Stephen Muggleton,et al.  Inverse entailment and progol , 1995, New Generation Computing.

[30]  Lise Getoor,et al.  Collective Classification in Network Data , 2008, AI Mag..

[31]  Lise Getoor,et al.  Effective label acquisition for collective classification , 2008, KDD.

[32]  Christos Faloutsos,et al.  Using ghost edges for classification in sparsely labeled networks , 2008, KDD.

[33]  Foster J. Provost,et al.  Classification in Networked Data: a Toolkit and a Univariate Case Study , 2007, J. Mach. Learn. Res..

[34]  Michelangelo Ceci,et al.  Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach , 2007, PKDD.

[35]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[36]  Melanie Hilario,et al.  Learning to combine distances for complex representations , 2007, ICML '07.

[37]  Michelangelo Ceci,et al.  Spatial associative classification: propositional vs structural approach , 2006, Journal of Intelligent Information Systems.

[38]  Raymond J. Mooney,et al.  Combining Bias and Variance Reduction Techniques for Regression Trees , 2005, ECML.

[39]  Peter A. Flach,et al.  Naive Bayesian Classification of Structured Data , 2004, Machine Learning.

[40]  Jennifer Neville,et al.  Why collective inference improves relational classification , 2004, KDD.

[41]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[42]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[43]  Peter A. Flach,et al.  Comparative Evaluation of Approaches to Propositionalization , 2003, ILP.

[44]  Michelangelo Ceci,et al.  Mr-SBC: A Multi-relational Naïve Bayes Classifier , 2003, PKDD.

[45]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[46]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[47]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[48]  Arno J. Knobbe,et al.  Propositionalisation and Aggregates , 2001, PKDD.

[49]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[50]  Hendrik Blockeel,et al.  Top-Down Induction of First Order Logical Decision Trees , 1998, AI Commun..

[51]  Liviu Badea.  Reifying Concepts in Description Logics , 1997, IJCAI.

[52]  R. Mike Cameron-Jones,et al.  FOIL: A Midterm Report , 1993, ECML.

[53]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .